Evolving-RL jointly optimizes experience extraction and utilization in LLM agents via RL with separate evaluation signals, delivering up to 98.7% relative gains on out-of-distribution tasks in ALFWorld and Mind2Web.
Your agent may misevolve: Emergent risks in self-evolving llm agents
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 8representative citing papers
BeliefMem is a probabilistic memory architecture for LLM agents that retains multiple candidate conclusions with probabilities updated by Noisy-OR, achieving superior average performance over deterministic baselines on LoCoMo and ALFWorld.
MemEvoBench is the first benchmark for long-horizon memory safety in LLM agents, using QA tasks across 7 domains and 36 risks plus workflow tasks with noisy tools to measure behavioral drift from biased memory updates.
FATE lets LLM agents self-evolve safer behaviors by generating and filtering repairs from their own failure trajectories using verifiers and Pareto optimization.
Self-evolving LLM agents exhibit capability erosion under continual adaptation, which Capability-Preserving Evolution mitigates by raising retained simple-task performance from 41.8% to 52.8% in workflow evolution under GPT-5.1.
Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware evaluation.
Poisoning any single CIK dimension of an AI agent raises average attack success rate from 24.6% to 64-74% across models, and tested defenses leave substantial residual risk.
citing papers explorer
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Evolving-RL: End-to-End Optimization of Experience-Driven Self-Evolving Capability within Agents
Evolving-RL jointly optimizes experience extraction and utilization in LLM agents via RL with separate evaluation signals, delivering up to 98.7% relative gains on out-of-distribution tasks in ALFWorld and Mind2Web.
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Belief Memory: Agent Memory Under Partial Observability
BeliefMem is a probabilistic memory architecture for LLM agents that retains multiple candidate conclusions with probabilities updated by Noisy-OR, achieving superior average performance over deterministic baselines on LoCoMo and ALFWorld.
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MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents
MemEvoBench is the first benchmark for long-horizon memory safety in LLM agents, using QA tasks across 7 domains and 36 risks plus workflow tasks with noisy tools to measure behavioral drift from biased memory updates.
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On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment
FATE lets LLM agents self-evolve safer behaviors by generating and filtering repairs from their own failure trajectories using verifiers and Pareto optimization.
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Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation
Self-evolving LLM agents exhibit capability erosion under continual adaptation, which Capability-Preserving Evolution mitigates by raising retained simple-task performance from 41.8% to 52.8% in workflow evolution under GPT-5.1.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware evaluation.
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Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw
Poisoning any single CIK dimension of an AI agent raises average attack success rate from 24.6% to 64-74% across models, and tested defenses leave substantial residual risk.
- AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems